Distribution transformer load modeling using load research data

Distribution network analyses require accurate estimates of transformer loads; due to lack of field measurements, data used in these studies have various degrees of uncertainties. In order to take the expected uncertainties in demand into account, many previous papers have used fuzzy load models in their studies. However, the issue of deriving these models has not been discussed. To address this issue, an approach for building these fuzzy load models is proposed in this paper. In the first stage of the proposed method, customer class load profiles are constructed. Different from previous load profiling techniques, customer hourly load distributions obtained from load research are converted to fuzzy membership functions based on a possibility-probability consistency principle. With the customer class fuzzy load profiles, customer monthly power consumption, and feeder measurements, hourly loads of each distribution transformer on the feeder are estimated. The load data are not represented by a unique value, but by intervals with confidence levels. In the calculation of load estimates, fuzzy arithmetic is used. To verify the accuracy of the proposed method, feeder SCADA data and transformer load measurements are used. Test results indicate that the proposed method provides an accurate and flexible approach for building transformer load models that can be used in transformer management and distribution network analyses.